scholarly journals An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks

2019 ◽  
Vol 9 (5) ◽  
pp. 1010
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
A Priori ◽  
Classification Performance ◽  
Clock Recovery ◽  
Modulation Recognition ◽  
Attention Model ◽  
Important Research Topic ◽  
Automatic Modulation Recognition ◽  
Time Drift ◽  
Improved Model

Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals Deep Learning Aided Method for Automatic Modulation Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109063-109068 ◽  
Author(s):  
Cheng Yang ◽  
Zhimin He ◽  
Yang Peng ◽  
Yu Wang ◽  
Jie Yang
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Automatic Modulation Recognition

scholarly journals Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42841-42847 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Phase Offset ◽  
Automatic Modulation Recognition

scholarly journals Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

2020 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Recognition Performance ◽  
Harmful Effect ◽  
Ofdm System ◽  
Modulation Recognition ◽  
Phase Offset ◽  
Communications Systems ◽  
Automatic Modulation Recognition ◽  
Conventional Methods

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

scholarly journals Blind Channel Identification Aided Generalized Automatic Modulation Recognition Based on Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110722-110729 ◽  
Author(s):  
Hao Gu ◽  
Yu Wang ◽  
Sheng Hong ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Channel Identification ◽  
Blind Channel Identification ◽  
Blind Channel ◽  
Automatic Modulation Recognition

Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios

2019 ◽  
Vol 68 (4) ◽  
pp. 4074-4077 ◽  
Author(s):  
Yu Wang ◽  
Miao Liu ◽  
Jie Yang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Cognitive Radios ◽  
Data Driven ◽  
Modulation Recognition ◽  
Automatic Modulation Recognition

scholarly journals Automatic Modulation Recognition for MFSK Using Modified Covariance Method

2015 ◽  
Vol 5 (3) ◽  
pp. 429
Author(s):  
Hanan M.Hamee ◽  
Jafer Wadi
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Modulation Index ◽  
Modulation Classification ◽  
A Priori Information ◽  
Modulation Recognition ◽  
Digital Signals ◽  
Signal To Noise ◽  
Automatic Modulation Recognition ◽  
Priori Information

This paper presents modulation classification method capable of classifying<br />MFSK digital signals without a priori information using modified covariance<br />method. This method using for calculation features for FSK modulation<br />should have a good properties of sensitive with FSK modulation index and<br />insensitive with signal to noise ratio SNR variation. The numerical<br />simulations and investigation of the performance by the support vectors<br />machine one against all (SVM-OAA) as a classifier for classifying 6 digitally<br />modulated signals which gives probability of correction classification up to<br />85.85 at SNR=-15dB.

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